u/rikulauttia

Imagine two assistants.

Assistant A:

• strongest available model
• everything processed in the cloud
• monthly subscription
• requires internet

Assistant B:

• slightly weaker
• runs locally
• works offline
• your data never leaves the device
• one-time hardware cost

Which would you choose?

For casual questions I would probably use the cloud.

For emails, personal documents, company code and private data - local AI becomes much more interesting.

How much intelligence would you sacrifice for privacy and ownership?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

A lot of AI startups are built on APIs that their competitors can also access.

So what actually becomes defensible?

• proprietary data
• distribution
• deep workflow integration
• customer relationships
• better user experience
• lower prices
• industry knowledge
• network effects
• moving faster than everyone else

"Better prompts" does not feel like a long-term moat.

What AI product have you seen that genuinely becomes harder to copy as more people use it?

Not a research demo.

Not a humanoid doing one carefully rehearsed task.

A robot that millions of normal people would willingly buy.

What does it do?

• clean the entire house
• cook
• take care of elderly people
• handle laundry
• provide security
• carry things
• maintain the garden
• something else

My guess is the first mass-market winner will not look very human.

It will probably do one boring and painful task extremely well.

What task is valuable enough to justify the cost?

People already use AI for advice about relationships, money, careers, loneliness and mental health.

That can feel safer because the AI does not judge you.

But the conversation still exists somewhere inside a company’s system.

So which do you trust more:

• a human who might judge or repeat what you said
• an AI that feels private but is operated by a company

Would you ever use AI as a therapist, coach or close friend?

And what privacy guarantee would you need before being completely honest?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

A lot of AI tools are best at the tasks companies traditionally gave to junior employees.

• simple code
• basic research
• first drafts
• data cleaning
• documentation
• support responses
• routine analysis

That sounds great for productivity.

But senior engineers, researchers and managers do not appear from nowhere.

They become good by doing smaller tasks, making mistakes and receiving feedback.

If AI removes the beginner work - how are beginners supposed to become experts?

Are companies thinking about this at all?

Not because every AI post is false.

Because producing average content now costs almost nothing.

Thousands of:

• generic tutorials
• fake product reviews
• SEO articles
• copied opinions
• meaningless comments
• low effort videos
• fake personal stories

Even if 90% of it is technically correct, finding anything original becomes harder.

The internet might not die because AI lies.

It might die because useful information gets buried under infinite mediocre content.

What platform has handled this problem best so far?

And how should Reddit deal with it without banning useful AI-assisted posts?

Choose the highest level you would genuinely allow:

Level 1 - read files

Level 2 - edit files

Level 3 - send emails and messages

Level 4 - install software

Level 5 - use your credit card with a spending limit

Level 6 - deploy production code

Level 7 - operate without asking for approval

I’m somewhere around level 2 for general use and maybe level 4 inside a tightly controlled environment.

The models are becoming capable quickly.

Trust seems to be moving alot slower.

What is your level?

People in Europe are now supposed to receive clearer information in key situations when they are interacting with AI or seeing certain AI-generated content.

But I’m curious what happened in practice.

Did your company:

• add an AI disclosure
• label generated content
• update a chatbot
• change internal documentation
• receive new legal guidance
• do absolutely nothing

Not looking for theoretical summaries.

What changed inside real products and companies this week?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

The argument seems to have two extreme sides.

Side one:

Europe is creating basic rules before AI becomes impossible to control.

Side two:

Europe is regulating technology it is already behind in building.

I think both can be partly true.

Good rules might build trust and make adoption easier.

Badly implemented rules might make small European startups spend money on compliance while massive companies handle it easily.

For builders in Europe - has regulation made you build better systems?

Or has it mostly created uncertainty and paperwork?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Every few months someone declares that open models have caught up.

Then a closed lab releases something stronger.

Then open models become cheaper, smaller or easier to customize.

Maybe open source does not need to be number one on every benchmark.

Maybe its real role is:

• stopping one company from controlling everything
• allowing private and local deployment
• supporting research
• making AI accessible outside the richest companies
• forcing closed providers to lower prices

What would count as open-source AI genuinely "winning" to you?

Most usage?

Best model?

Most developer adoption?

Or simply preventing a monopoly?

Imagine Reddit, X, YouTube, LinkedIn and every news site showing a clear label:

• fully human
• AI assisted
• mostly AI generated
• unknown

Would this make the internet more trustworthy?

Or would people just learn to ignore the labels after a week?

The difficult part is also defining "AI assisted."

Spellcheck?

Rewriting one sentence?

Generating the entire post?

Where would you draw the line?

Writing the first version of code is only part of software engineering.

The annoying parts come later:

• understanding an old system
• debugging production
• explaining strange decisions
• handling migrations
• fixing security issues
• maintaining the same feature two years later

An agent can open a clean looking pull request.

But who owns that code when it breaks at 3am?

For developers already using coding agents heavily - are they genuinely reducing long-term work, or just creating code faster than teams can review it?

AI voices can now hesitate, laugh, interrupt and react in ways that feel alot more human than old voice assistants.

That is impressive.

It is also slightly uncomfortable.

Should an AI be allowed to imitate:

• your family member
• a dead person
• your doctor
• your manager
• a celebrity
• a political leader
• anyone who gave permission

Where should the line be?

And should every AI voice be forced to identify itself as AI at the beginning of a conversation?

Let’s make predictions that can actually be proven wrong.

No:

• "AI will become more powerful"
• "AGI is coming"
• "everything will change"

Write one specific prediction about what will be true by 13 July 2028.

Examples of the level of specificity:

• a named job will have 30% fewer entry-level openings
• most software teams will use agents that open their own pull requests
• a household robot will sell more than one million units
• one open model will be the default choice for enterprise AI
• AI generated video will be used in a majority of online ads

Add your confidence percentage.

I’ll start:

Prediction: Most professional developers will supervise multiple coding agents every day, but companies will still require a human to approve production changes.

Confidence: 75%

Your turn.

Most people currently experience AI through text, voice or generated media.

But the largest long-term impact might come from systems that interact with the physical world:

• robots inside factories
• autonomous laboratories
• smarter energy systems
• hospital equipment
• agriculture
• logistics
• construction
• vehicles and infrastructure

A chatbot can give advice.

A physical AI system can actually change what happens in the world.

The problem is that atoms are much less forgiving than software. A hallucinated paragraph is annoying. A hallucinated movement can destroy equipment or hurt someone.

What physical workflow do you think is most ready for AI right now?

And what is still being massively overhyped?

The largest companies have:

• more compute
• more data
• higher salaries
• massive engineering teams
• the ability to run experiments universities simply cannot afford

That makes it hard to imagine academia competing directly on frontier model development.

But academia may still be better positioned to work on questions that:

• have no immediate commercial return
• challenge the assumptions of major AI companies
• require long-term independent research
• investigate safety, society or fundamental science
• should remain open to everyone

So what is the role of academic AI research now?

Is it becoming less relevant - or is independent research more important than ever?

Especially interested in answers from researchers who have worked in both environments.